Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control

Herbert Buchner, Eike Petersen, Marcus Eger, Philipp Rostalski

Abstract

The electromyogram (EMG) is an important tool for assessing the activity of a muscle and thus also a valuable measure for the diagnosis and control of respiratory support. In this article we propose convolutive blind source separation (BSS) as an effective tool to pre-process surface electromyogram (sEMG) data of the human respiratory muscles. Specifically, the problem of discriminating between inspiratory, expiratory and cardiac muscle activity is addressed, which currently poses a major obstacle for the clinical use of sEMG for adaptive ventilation control. It is shown that using the investigated broadband algorithm, a clear separation of these components can be achieved. The algorithm is based on a generic framework for BSS that utilizes multiple statistical signal characteristics. Apart from a four-channel FIR structure, there are no further restrictive assumptions on the demixing system.

Original languageEnglish
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Number of pages4
PublisherIEEE
Publication date13.10.2016
Pages3626-3629
Article number7591513
ISBN (Print)978-1-4577-0219-8
ISBN (Electronic)978-1-4577-0220-4
DOIs
Publication statusPublished - 13.10.2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Disney's Contemporary Resort Orlando, Orlando, United States
Duration: 16.08.201620.08.2016

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